A notable characteristic of many cyclical demand systems is the presence of periods of peak demand, which are typically of relatively short duration and occur at more or less fixed times within the overall demand cycle. The residential electricity grid is a well-known example of such a cyclical demand system, in which the daily demand cycle typically has two peak periods that occur during the morning and evening hours. Besides this daily demand cycle, the residential electricity grid may also have secondary weekly or annual demand cycles. Peak periods may also be identified within the secondary demand cycles, and may be more easily discerned when the historical demand data is appropriately aggregated to smooth out the higher-frequency demand cycles, e.g., by using daily aggregated data to examine the weekly demand cycle, or by using weekly or monthly aggregated data to examine the annual demand cycle.
A concern in such a cyclical demand system is the need to design and provision the generation and transmission resources for these short-duration peak periods. This requirement invariably entails higher capital and operating costs for the electricity grid. These costs are typically borne by all consumers, including those who have little or no electricity usage during the peak periods. The resulting pricing inequities are sometimes addressed by adopting time-of-use pricing in order to differentiate between the usage during the peak and off-peak periods of the demand cycle.
With the advent of smart metering and two-way communications in the electricity grid, referred to as the “Smart Grid”, this static time-of-use pricing approach may increasingly be replaced by a more dynamic real-time pricing approach with considerably more flexibility for managing the demand profile within the overall demand cycle. The newest dynamic approaches that are being considered rely on providing consumers with accurate short-term projections of their price of electricity over the upcoming daily demand cycles. Given these projected prices, consumers may plan to limit their usage during high-price peak periods or to migrate their usage to low-price off-peak periods; either action leading to a more uniform load-curve profile over the entire demand cycle, which is an operational goal for the electricity grid.
From the perspective of short-term load forecasting in the residential electricity grid, the entire daily demand cycle can be considered as an important unit for understanding the demand substitution effects due to dynamic pricing. This is because residential (and some business) consumers typically have some flexibility in scheduling their daily usage requirements, but considerably less flexibility in moving their usage from one day to another. For example, space heating tends to comprise the largest component of residential electricity load during the winter months. In this case, the individual heating schedules may be flexible enough to incorporate preheating by a few hours in anticipation of higher prices during the ensuing peak period. However, such a preheating strategy is unlikely to be effective across the boundaries of the daily demand cycle.
The impact of dynamic pricing incentives on the short-term demand reduction and demand substitution within the daily load cycle is of interest from the operational perspective in the Smart Grid. However, there has been little effort in developing techniques to incorporate these dynamic pricing effects into the modeling and forecasting of the daily load cycle.